A real-time defect detection method, device and medium based on carbon fiber winding

Abstract

The present application provides a real-time defect detection method, device, and medium based on carbon fiber winding, which relates to the technical field of carbon fiber layup detection. The method includes: obtaining real-time environmental data of carbon fiber winding, and performing multimodal collaborative detection on the real-time environmental data of carbon fiber winding to obtain a real-time carbon fiber defect data group; performing multimodal spatiotemporal synchronous calibration on the real-time carbon fiber defect data group to determine carbon fiber defect synchronization data; performing strategy-level fusion on the carbon fiber defect synchronization data to obtain preliminary winding state determination data; based on the preliminary winding state determination data, determining multimodal weight update data through carbon fiber winding process stage analysis; and determining carbon fiber real-time winding defects through carbon fiber defect type analysis based on the modal weight update data. The present application solves the technical problem of poor anti-interference ability of a single parameter analysis process in carbon fiber winding detection through the above method.

Description

Technical Field

[0001] The present application relates to the technical field of carbon fiber layup detection, and in particular to a real-time defect detection method, device, and medium based on carbon fiber winding. Background Art

[0002] Carbon fiber composites combine high-strength carbon fibers with a resin matrix to create lightweight, high-strength structural materials. They are widely used in aerospace (such as aircraft wing spars and satellite mounts), new energy vehicles (battery housings and vehicle body structures), and wind power (blade beams). Their core manufacturing process includes prepreg placement, lamination and curing, and molding, with the quality of the layup directly determining the final product's performance.

[0003] As a key automation module for carbon fiber layup, the carbon fiber winding robot's real-time layup inspection determines its quality. Existing technologies typically analyze layup status using non-contact methods like visual inspection and eddy current testing. However, single-parameter analysis can be affected by environmental interference, leading to abnormal test results and insufficient robustness of carbon fiber inspection systems. Summary of the Invention

[0004] The embodiments of the present application provide a real-time defect detection method, device, and medium based on carbon fiber winding, which solve the technical problem of poor anti-interference ability of a single parameter analysis process in carbon fiber winding detection.

[0005] In a first aspect, an embodiment of the present application provides a real-time defect detection method based on carbon fiber winding, characterized in that the method includes: acquiring real-time environmental data of carbon fiber winding, and performing multimodal collaborative detection on the real-time environmental data of carbon fiber winding to obtain a real-time defect data group of carbon fiber; performing multimodal spatiotemporal synchronous calibration on the real-time defect data group of carbon fiber to determine carbon fiber defect synchronization data; performing strategy-level fusion on the carbon fiber defect synchronization data to obtain preliminary judgment data of winding status; based on the preliminary judgment data of winding status, determining multimodal weight update data through process stage analysis of carbon fiber winding; determining the real-time winding defect of carbon fiber through carbon fiber defect type analysis according to the modal weight update data.

[0006] In one implementation of the present application, the real-time environmental data of carbon fiber winding also includes: carbon fiber plies, local mechanical vibration data, and broadband acoustic emission signals; multimodal collaborative detection is performed on the real-time environmental data of carbon fiber winding to obtain a carbon fiber real-time defect data group, specifically including: high-frequency eddy current array inversion analysis of the carbon fiber plies to obtain carbon fiber ply status data; wherein the carbon fiber ply status data includes: fiber arrangement direction, deep defect data; surface vibration response analysis is performed on the local mechanical vibration data to obtain local mechanical impedance; wherein the surface vibration response analysis includes: swept frequency mechanical vibration processing, surface laser vibration analysis; based on the broadband acoustic emission signal, the ply defect type parameters are determined through soundprint feature recognition; the carbon fiber real-time defect data group is obtained according to the carbon fiber ply status data, local mechanical impedance and ply defect type parameters.

[0007] In one implementation of the present application, a multimodal spatiotemporal synchronization calibration is performed on the carbon fiber real-time defect data group to determine the carbon fiber defect synchronization data, specifically including: performing multimodal spatial calibration on the carbon fiber real-time defect data, and performing feature coordinate conversion on the feature coordinates obtained by the multimodal spatial calibration to obtain the defect point space coordinates; wherein the feature coordinate conversion includes: electrical marker point coordinate conversion, stiffness mutation zone coordinate conversion, and voiceprint positioning coordinate conversion; obtaining the data processing delay of the preset sensor, and obtaining multimodal time synchronization data through reverse delay compensation based on the data processing delay; determining the carbon fiber defect synchronization data according to the defect point space coordinates and the multimodal time synchronization data.

[0008] In one implementation of the present application, the carbon fiber defect synchronization data is strategically integrated to obtain preliminary judgment data of the winding state, specifically including: performing basic confidence configuration analysis on the carbon fiber defect synchronization data to obtain the modal confidence corresponding to each mode; wherein the modal confidence includes: eddy current modal confidence, mechanical impedance modal confidence, and acoustic emission modal confidence; based on the modal confidence, the first evidence synthesis data is determined through reliability weight correction; the first evidence synthesis data is dynamically weighted optimized to obtain the second evidence synthesis data; based on the second evidence synthesis data, the preliminary judgment data of the winding state is obtained through modal conflict resolution analysis.

[0009] In one implementation of the present application, based on the preliminary judgment data of the winding state, the multimodal weight update data is determined through the process stage analysis of the carbon fiber winding, specifically including: obtaining the current process stage, and based on the current process stage, obtaining the multimodal weight adjustment data through the modal weight configuration; wherein, the current process stage includes: the initial laying stage, the lamination stage, and the emergency working condition; according to the preliminary judgment data of the winding state and the multimodal weight adjustment data, the multimodal weight update data is determined through the modal weight update.

[0010] In one implementation of the present application, the real-time winding defect of the carbon fiber is determined through carbon fiber defect type analysis based on the modal weight update data, specifically including: determining the defect type weight data through defect type-guided weight allocation based on the modal weight update data; performing dynamic weight adaptive processing on the defect type weight data to determine the multimodal quality weight data; and determining the real-time winding defect of the carbon fiber through comprehensive confidence threshold judgment based on the multimodal quality weight data.

[0011] In one implementation of the present application, after updating data according to modal weights and determining the real-time carbon fiber winding defects through carbon fiber defect type analysis, the method also includes: dynamically adjusting the winding equipment parameters for the real-time carbon fiber winding defects to determine the equipment adjustment parameters; wherein the equipment adjustment parameters include: pressure dynamic adjustment parameters, laying speed optimization parameters, defect path avoidance path planning parameters; based on the equipment adjustment parameters, the equipment is closed-loop controlled to complete the equipment adjustment corresponding to the real-time carbon fiber winding defects.

[0012] In one implementation of the present application, the winding equipment parameters are dynamically adjusted for real-time winding defects of carbon fiber to determine the equipment adjustment parameters, specifically including: constructing a pressure compensation relationship matrix, and based on the pressure compensation relationship matrix, obtaining the pressure dynamic adjustment parameters through adaptive PID control analysis; obtaining the density change data of the carbon fiber winding layer, and performing gradient descent optimization of the data coefficients on the density change data to obtain the laying speed optimization parameters; obtaining the position of the defect area, and according to the position of the defect area, determining the defect path avoidance path planning parameters through B-spline curve optimization analysis.

[0013] In a second aspect, an embodiment of the present application also provides a real-time defect detection device based on carbon fiber winding, characterized in that the device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: obtain real-time environmental data of carbon fiber winding, and perform multimodal collaborative detection on the real-time environmental data of carbon fiber winding to obtain a real-time defect data group of carbon fiber; perform multimodal spatiotemporal synchronous calibration on the real-time defect data group of carbon fiber to determine carbon fiber defect synchronous data; perform strategy-level fusion on the carbon fiber defect synchronous data to obtain preliminary judgment data of the winding state; based on the preliminary judgment data of the winding state, determine the multimodal weight update data through the process stage analysis of the carbon fiber winding; determine the real-time winding defect of carbon fiber through the carbon fiber defect type analysis according to the modal weight update data.

[0014] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium for real-time defect detection based on carbon fiber winding, which stores computer executable instructions, and is characterized in that the computer executable instructions are set to: obtain real-time environmental data of carbon fiber winding, and perform multimodal collaborative detection on the real-time environmental data of carbon fiber winding to obtain a real-time defect data group of carbon fiber; perform multimodal spatiotemporal synchronous calibration on the real-time defect data group of carbon fiber to determine the carbon fiber defect synchronization data; perform strategy-level fusion on the carbon fiber defect synchronization data to obtain preliminary judgment data of the winding state; based on the preliminary judgment data of the winding state, determine the multimodal weight update data through the process stage analysis of the carbon fiber winding; determine the real-time winding defect of the carbon fiber through the carbon fiber defect type analysis according to the modal weight update data.

[0015] The embodiments of the present application provide a real-time defect detection method, device and medium based on carbon fiber winding. Through multimodal fusion perception of carbon fiber, intelligent decision-making of carbon fiber ply defects and dynamic closed-loop control, the technical problem of poor anti-interference ability of a single parameter analysis process in carbon fiber winding detection is solved, multi-dimensional carbon fiber laying defect analysis is realized, and the quality stability of carbon fiber laying and the system stability of the carbon fiber winding robot are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 A flow chart of a real-time defect detection method based on carbon fiber winding provided in an embodiment of the present application;

[0018] Figure 2 A schematic diagram of the internal structure of a real-time defect detection device based on carbon fiber winding provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiments of the present application provide a real-time defect detection method, device and medium based on carbon fiber winding. Through multimodal fusion perception of carbon fiber, intelligent decision-making of carbon fiber ply defects and dynamic closed-loop control, the technical problem of poor anti-interference ability of a single parameter analysis process in carbon fiber winding detection is solved, multi-dimensional carbon fiber laying defect analysis is realized, and the quality stability of carbon fiber laying and the system stability of the carbon fiber winding robot are improved.

[0021] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0022] Figure 1 This is a flow chart of a real-time defect detection method based on carbon fiber winding provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a real-time defect detection method based on carbon fiber winding, which specifically includes the following steps:

[0023] Step 101: Acquire real-time environmental data of carbon fiber winding, and perform multimodal collaborative detection on the real-time environmental data of carbon fiber winding to obtain a real-time carbon fiber defect data set.

[0024] For example, by performing multimodal collaborative detection on the real-time environmental data of carbon fiber winding, it is possible to process the influencing parameters that affect the laying quality of the carbon fiber ply during the carbon fiber laying process under their respective modes, thereby realizing multimodal detection of environmental data and improving the data breadth of the background data for ply quality detection.

[0025] Specifically, the real-time environmental data of carbon fiber winding also includes: carbon fiber plies, local mechanical vibration data, and broadband acoustic emission signals; multimodal collaborative detection is performed on the real-time environmental data of carbon fiber winding to obtain a carbon fiber real-time defect data group, including: high-frequency eddy current array inversion analysis of the carbon fiber plies to obtain carbon fiber ply status data; wherein, the carbon fiber ply status data includes: fiber arrangement direction, deep defect data; surface vibration response analysis is performed on the local mechanical vibration data to obtain local mechanical impedance; wherein, the surface vibration response analysis includes: swept frequency mechanical vibration processing, surface laser vibration analysis; based on the broadband acoustic emission signal, the ply defect type parameters are determined through soundprint feature recognition; according to the carbon fiber ply status data, the carbon fiber real-time defect data group is obtained.

[0026] In one embodiment, during the carbon fiber layup process, multimodal sensors collect layup status data in real time. A high-frequency eddy current array sensor operates in a swept frequency mode between 100kHz and 10MHz, acquiring conductive anisotropy data on the fiber arrangement. The sampling rate per channel is 2kHz, generating a 64×64 impedance phase matrix to determine the carbon fiber layup status data.

[0027] Furthermore, the piezoelectric actuator was synchronously activated to generate a swept-frequency mechanical vibration of 10-1000 Hz. A laser Doppler vibrometer (LDV) was used to measure the surface vibration velocity response with an accuracy of 0.1 μm, and the dynamic stiffness value was calculated to determine the local mechanical impedance.

[0028] Furthermore, the broadband acoustic emission sensor is synchronously activated to capture the acoustic emission signal of the winding robot in the 20kHz-2MHz frequency band at a sampling rate of 50MHz, and the soundprint features are extracted through wavelet packet transform to determine the ply defect type parameters.

[0029] Finally, a calibration plate can be fixed on the surface of the carbon fiber ply, with common marking points of three modes on the calibration plate; the conductive characteristics of the marking points are scanned by eddy current array, the stiffness characteristics of the marking points are scanned by mechanical impedance, and the knocking soundprints of the marking points are captured by acoustic sensors, so as to establish a multi-modal spatial mapping relationship and obtain a real-time defect data set of carbon fiber.

[0030] Step 102: Perform multi-modal spatiotemporal synchronization calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronization data.

[0031] For example, the real-time defect data of carbon fiber are all obtained through non-contact data acquisition. In order to achieve the spatiotemporal alignment of multimodal data, it is necessary to synchronize the carbon fiber ply state data, local mechanical impedance and ply defect type parameters through multimodal spatiotemporal synchronous calibration.

[0032] Specifically, a multimodal spatiotemporal synchronization calibration is performed on the carbon fiber real-time defect data group to determine the carbon fiber defect synchronization data, including: multimodal spatial calibration of the carbon fiber real-time defect data, and characteristic coordinate conversion of the characteristic coordinates obtained by the multimodal spatial calibration to obtain the defect point space coordinates; wherein the characteristic coordinate conversion includes: electrical marker point coordinate conversion, stiffness mutation zone coordinate conversion, and voiceprint positioning coordinate conversion; obtaining the data processing delay of the preset sensor, and obtaining multimodal time synchronization data through reverse delay compensation based on the data processing delay; determining the carbon fiber defect synchronization data according to the defect point space coordinates and the multimodal time synchronization data.

[0033] In one embodiment, to achieve spatiotemporal alignment of multimodal data, the IEEE 1588 Precision Time Protocol (PTP) is used for hardware-level clock synchronization to ensure that the time error is ≤1μs; a spatial coordinate transformation matrix is ​​established through a multimodal calibration plate, limiting the registration error to ≤0.1mm.

[0034] First, a multimodal calibration plate is fixed on the surface of the carbon fiber ply. The calibration plate is equipped with conductive marking points (copper), stiffness mutation areas (silicone embedded) and sound reflection protrusions (ceramic material) to meet the coordinate calibration requirements under three modes.

[0035] Since the coordinate systems of the three modes are not the same, the coordinate systems need to be uniformly transformed to establish a unified spatial coordinate system transformation matrix to achieve spatial synchronization.

[0036] It should be noted that the coordinate transformation of electrical marker points, the coordinate transformation of stiffness mutation zone, and the coordinate transformation of voiceprint positioning can all be used. The purpose of coordinate transformation is to build a unified coordinate system, and users can perform the transformation according to their own needs.

[0037] Finally, the data processing delay of each sensor is monitored in real time, and reverse delay compensation is applied to the subsequent fusion module to meet time synchronization.

[0038] Step 103: Perform strategy-level fusion on the carbon fiber defect synchronization data to obtain preliminary winding state determination data.

[0039] For example, by integrating the detection results of multimodal sensors (eddy current, mechanical impedance, and acoustic emission) and combining physical constraints with dynamic weight optimization, the credibility of the preliminary judgment data of the winding state is improved, and the impact of external factors on the analysis of the carbon fiber layup state is reduced.

[0040] Specifically, the carbon fiber defect synchronization data is strategically integrated to obtain preliminary judgment data of the winding state, specifically including: performing basic confidence configuration analysis on the carbon fiber defect synchronization data to obtain the modal confidence corresponding to each mode; wherein, the modal confidence includes: eddy current modal confidence, mechanical impedance modal confidence, and acoustic emission modal confidence; based on the modal confidence, the first evidence synthesis data is determined through reliability weight correction; the first evidence synthesis data is dynamically weighted optimized to obtain the second evidence synthesis data; based on the second evidence synthesis data, the preliminary judgment data of the winding state is obtained through modal conflict resolution analysis.

[0041] In one embodiment, first, basic confidence distribution functions are defined for the three modes of high-frequency eddy current, mechanical impedance, and acoustic emission, respectively. The eddy current modal confidence is calculated based on the impedance phase consistency, the mechanical impedance modal confidence is calculated based on the stiffness deviation rate, and the acoustic emission modal confidence can be output through CNN-based classification probability.

[0042] Then, the first DS evidence synthesis data is constructed, and the DS evidence synthesis data is corrected by the modal reliability weight to determine the second evidence synthesis data. The second evidence synthesis data is subjected to acoustic modal-dominated conflict resolution to obtain preliminary entanglement state judgment data.

[0043] It should be noted that the conflict resolution dominated by the acoustic mode is determined by a preset threshold value, which is used to resolve the conflict of the remaining two modes when the acoustic emission mode has an adverse impact on the initial determination of the entanglement state.

[0044] Step 104: Based on the preliminary determination data of the winding state, the multimodal weight update data is determined by analyzing the process stages of the carbon fiber winding.

[0045] For example, in order to meet the status of the carbon fiber winding robot in different laying stages, the multimodal weights of different process stages are stepped so that the defects of the carbon fiber plies in different process stages are close to the actual state, thereby improving the accuracy of carbon fiber defect analysis.

[0046] Specifically, based on the preliminary judgment data of the winding state, the multimodal weight update data is determined through the process stage analysis of carbon fiber winding, including: obtaining the current process stage, and based on the current process stage, obtaining the multimodal weight adjustment data through the modal weight configuration; wherein, the current process stage includes: the initial laying stage, the lamination stage, and the emergency working condition; according to the preliminary judgment data of the winding state and the multimodal weight adjustment data, the multimodal weight update data is determined through the modal weight update.

[0047] In one embodiment, the modal weight coefficient is set according to the process stage, and the multimodal weight adjustment data is obtained through the modal weight configuration, which is explained by the following formula:

[0048] (1)

[0049] in, Score the data quality to quantify the reliability of the current data of each modality;

[0050] is the modal default weight ( =3, representing three weights; similarly, Also 3), the initial weights are preset based on the prior knowledge of different carbon fiber placement process stages, reflecting the inherent importance of each mode in a specific stage;

[0051] In order to control the weight distribution ratio of default weight and real-time data quality, in this application, The value is controlled between 0.6-0.7;

[0052] For the Real-time data quality scores for each modality ( Value and Same, 1-3);

[0053] The sum of the real-time data quality scores representing all modalities;

[0054] For the The weights after modal adjustment ( The value range is 1-3).

[0055] Step 105: Update the data according to the modal weight, and determine the real-time winding defects of the carbon fiber by analyzing the carbon fiber defect types.

[0056] For example, after determining the modal weight update data, the analysis status of the Taixuanwei winding robot under each placement process can be configured. Through the carbon fiber defect type analysis, the defect situation of the carbon fiber ply at a certain process stage can be determined, realizing non-contact analysis of carbon fiber ply defects.

[0057] Specifically, according to the modal weight update data, the carbon fiber real-time winding defect is determined through carbon fiber defect type analysis, which specifically includes: based on the modal weight update data, the defect type weight data is determined through defect type-guided weight allocation; the defect type weight data is dynamically weighted adaptively processed to determine the multimodal quality weight data; according to the multimodal quality weight data, the carbon fiber real-time winding defect is determined through comprehensive confidence threshold judgment.

[0058] Furthermore, after updating the data according to the modal weight and determining the real-time winding defect of the carbon fiber through carbon fiber defect type analysis, the method also includes: dynamically adjusting the winding equipment parameters for the real-time winding defect of the carbon fiber to determine the equipment adjustment parameters; wherein the equipment adjustment parameters include: dynamic pressure adjustment parameters, placement speed optimization parameters, and defect path avoidance path planning parameters; based on the equipment adjustment parameters, the equipment is closed-loop controlled to complete the equipment adjustment corresponding to the real-time winding defect of the carbon fiber.

[0059] Furthermore, the winding equipment parameters are dynamically adjusted for the real-time winding defects of the carbon fiber to determine the equipment adjustment parameters, specifically including: constructing a pressure compensation relationship matrix, and based on the pressure compensation relationship matrix, obtaining the pressure dynamic adjustment parameters through adaptive PID control analysis; obtaining the density change data of the carbon fiber winding layer, and performing gradient descent optimization of the data coefficients of the density change data to obtain the laying speed optimization parameters; obtaining the position of the defect area, and according to the position of the defect area, determining the defect path avoidance path planning parameters through B-spline curve optimization analysis.

[0060] In one embodiment, the placement process parameters are adjusted in real time based on the fusion results, and a pressure gradient compensation strategy is adopted for fiber wrinkle defects.

[0061] Firstly, the relationship matrix between defect type and pressure compensation amount is established, and the dynamic tuning parameters are determined through adaptive PID control.

[0062] Then, in order to ensure the stability of carbon fiber placement when defects occur, the density change data is optimized by gradient descent of the data coefficient. When the pore density exceeds the preset threshold during the lamination stage, the carbon fiber ply movement speed is reduced to further reduce the porosity.

[0063] Finally, due to the layup process limitations of the defective part, the defective area is used as an obstacle to generate a smooth avoidance path to reduce the impact of uneven pressure on the surrounding carbon fiber plies caused by the defect.

[0064] Furthermore, the original path is interpolated and corrected through B-spline curve optimization analysis to determine the defect path avoidance path planning parameters.

[0065] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, this application embodiment also provides a real-time defect detection device based on carbon fiber winding, the structure of which is as follows: Figure 2 shown.

[0066] Figure 2 This is a schematic diagram of the internal structure of a real-time defect detection device based on carbon fiber winding provided in an embodiment of the present application. Figure 2 As shown, the equipment includes:

[0067] at least one processor 201;

[0068] and, a memory 202 communicatively coupled to the at least one processor;

[0069] The memory 202 stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0070] Real-time environmental data of carbon fiber winding is acquired, and multimodal collaborative detection is performed on the real-time environmental data of carbon fiber winding to obtain a real-time defect data group of carbon fiber; multimodal spatiotemporal synchronous calibration is performed on the real-time defect data group of carbon fiber to determine the synchronous data of carbon fiber defects; strategy-level fusion is performed on the synchronous data of carbon fiber defects to obtain preliminary judgment data of winding status; based on the preliminary judgment data of winding status, multimodal weight update data is determined through analysis of the process stages of carbon fiber winding; according to the modal weight update data, the real-time winding defects of carbon fiber are determined through analysis of the carbon fiber defect types.

[0071] Some embodiments of the present application provide corresponding Figure 1 A non-volatile computer storage medium for real-time defect detection based on carbon fiber winding, storing computer executable instructions, wherein the computer executable instructions are set to:

[0072] Real-time environmental data of carbon fiber winding is acquired, and multimodal collaborative detection is performed on the real-time environmental data of carbon fiber winding to obtain a real-time defect data group of carbon fiber; multimodal spatiotemporal synchronous calibration is performed on the real-time defect data group of carbon fiber to determine the synchronous data of carbon fiber defects; strategy-level fusion is performed on the synchronous data of carbon fiber defects to obtain preliminary judgment data of winding status; based on the preliminary judgment data of winding status, multimodal weight update data is determined through analysis of the process stages of carbon fiber winding; according to the modal weight update data, the real-time winding defects of carbon fiber are determined through analysis of the carbon fiber defect types.

[0073] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from the other embodiments. In particular, the IoT device and media embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0074] The system and medium provided in the embodiments of the present application correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0075] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0076] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0080] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0081] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0082] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0083] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A real-time defect detection method based on carbon fiber winding, characterized in that: The method comprises: Acquiring real-time carbon fiber winding environmental data, and performing multimodal collaborative detection on the real-time carbon fiber winding environmental data to obtain a real-time carbon fiber defect data set; performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data group to determine carbon fiber defect synchronization data; Performing strategic-level fusion on the carbon fiber defect synchronization data to obtain preliminary winding state determination data; Based on the preliminary winding state determination data, multimodal weight update data is determined by analyzing the process stages of carbon fiber winding; According to the modal weight update data, the real-time winding defects of the carbon fiber are determined by analyzing the carbon fiber defect types; The carbon fiber winding real-time environmental data also includes: carbon fiber layup, local mechanical vibration data, and broadband acoustic emission signals; Multimodal collaborative detection is performed on the carbon fiber winding real-time environmental data to obtain a carbon fiber real-time defect data set, specifically including: Performing high-frequency eddy current array inversion analysis on the carbon fiber ply to obtain carbon fiber ply state data; wherein the carbon fiber ply state data includes: fiber arrangement direction and deep defect data; Performing surface vibration response analysis on the local mechanical vibration data to obtain local mechanical impedance; wherein the surface vibration response analysis includes: frequency sweep mechanical vibration processing and surface laser vibrometer analysis; Based on the broadband acoustic emission signal, determining ply defect type parameters through acoustic print feature recognition; Obtaining the carbon fiber real-time defect data set according to the carbon fiber ply state data, the local mechanical impedance, and the ply defect type parameter; Performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronization data specifically includes: Performing multimodal spatial calibration on the real-time defect data of the carbon fiber, and performing feature coordinate conversion on the feature coordinates obtained by the multimodal spatial calibration to obtain the spatial coordinates of the defect point; wherein the feature coordinate conversion includes: electrical marker point coordinate conversion, stiffness mutation zone coordinate conversion, and voiceprint positioning coordinate conversion; Acquire a data processing delay of a preset sensor, and obtain multimodal time synchronization data by performing reverse delay compensation based on the data processing delay; Determining the carbon fiber defect synchronization data according to the defect point spatial coordinates and the multimodal time synchronization data; The carbon fiber defect synchronization data is strategically integrated to obtain preliminary winding status determination data, specifically including: Performing basic confidence level configuration analysis on the carbon fiber defect synchronization data to obtain modal confidence levels corresponding to various modes; wherein the modal confidence levels include: eddy current modal confidence level, mechanical impedance modal confidence level, and acoustic emission modal confidence level; Based on the modal confidence, first evidence synthesis data is determined through reliability weight correction; Performing dynamic weight optimization on the first evidence synthesis data to obtain second evidence synthesis data; The data of the second evidence synthesis is analyzed through modal conflict resolution to obtain preliminary determination data of the entanglement state.

2. The real-time defect detection method based on carbon fiber winding according to claim 1 is characterized in that: Based on the preliminary winding state determination data, the multimodal weight update data is determined through the carbon fiber winding process stage analysis, specifically including: Obtaining a current process stage, and obtaining multimodal weight adjustment data based on the current process stage through modal weight configuration; wherein the current process stage includes: initial layup stage, lamination stage, and emergency working condition; The multimodal weight update data is determined by updating the modal weight according to the preliminary determination data of the entanglement state and the multimodal weight adjustment data.

3. The real-time defect detection method based on carbon fiber winding according to claim 1 is characterized in that: According to the modal weight update data, the carbon fiber real-time winding defects are determined by analyzing the carbon fiber defect types, specifically including: Determining defect type weight data through defect type-guided weight allocation based on the modal weight update data; Performing dynamic weight adaptive processing on the defect type weight data to determine multimodal quality weight data; The real-time winding defect of the carbon fiber is determined based on the multimodal mass weight data through a comprehensive confidence threshold judgment.

4. The real-time defect detection method based on carbon fiber winding according to claim 1 is characterized in that: After updating the data according to the modal weight and determining the real-time carbon fiber winding defect through carbon fiber defect type analysis, the method further includes: Dynamically adjusting winding equipment parameters according to the real-time winding defects of the carbon fiber to determine equipment adjustment parameters; wherein the equipment adjustment parameters include: dynamic pressure adjustment parameters, placement speed optimization parameters, and defect path avoidance path planning parameters; Based on the equipment adjustment parameters, equipment closed-loop control is performed to complete equipment adjustment corresponding to the real-time winding defects of the carbon fiber.

5. The real-time defect detection method based on carbon fiber winding according to claim 4 is characterized in that: Dynamically adjusting the winding equipment parameters for the real-time winding defects of the carbon fiber to determine the equipment adjustment parameters specifically includes: Constructing a pressure compensation relationship matrix, and obtaining the pressure dynamic adjustment parameter through adaptive PID control analysis based on the pressure compensation relationship matrix; Obtaining density change data of the carbon fiber winding layer, and performing gradient descent optimization of data coefficients on the density change data to obtain the placement speed optimization parameter; The position of the defective area is obtained, and based on the position of the defective area, the defective path avoidance path planning parameters are determined through B-spline curve optimization analysis.

6. A real-time defect detection device based on carbon fiber winding, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: Acquiring real-time carbon fiber winding environmental data, and performing multimodal collaborative detection on the real-time carbon fiber winding environmental data to obtain a real-time carbon fiber defect data set; performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data group to determine carbon fiber defect synchronization data; Performing strategic-level fusion on the carbon fiber defect synchronization data to obtain preliminary winding state determination data; Based on the preliminary winding state determination data, multimodal weight update data is determined by analyzing the process stages of carbon fiber winding; According to the modal weight update data, the real-time winding defects of the carbon fiber are determined by analyzing the carbon fiber defect types; The carbon fiber winding real-time environmental data also includes: carbon fiber layup, local mechanical vibration data, and broadband acoustic emission signals; Multimodal collaborative detection is performed on the carbon fiber winding real-time environmental data to obtain a carbon fiber real-time defect data set, specifically including: Performing high-frequency eddy current array inversion analysis on the carbon fiber ply to obtain carbon fiber ply state data; wherein the carbon fiber ply state data includes: fiber arrangement direction and deep defect data; Performing surface vibration response analysis on the local mechanical vibration data to obtain local mechanical impedance; wherein the surface vibration response analysis includes: frequency sweep mechanical vibration processing and surface laser vibrometer analysis; Based on the broadband acoustic emission signal, determining ply defect type parameters through acoustic print feature recognition; Obtaining the carbon fiber real-time defect data set according to the carbon fiber ply state data, the local mechanical impedance, and the ply defect type parameter; Performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronization data specifically includes: Performing multimodal spatial calibration on the real-time defect data of the carbon fiber, and performing feature coordinate conversion on the feature coordinates obtained by the multimodal spatial calibration to obtain the spatial coordinates of the defect point; wherein the feature coordinate conversion includes: electrical marker point coordinate conversion, stiffness mutation zone coordinate conversion, and voiceprint positioning coordinate conversion; Acquire a data processing delay of a preset sensor, and obtain multimodal time synchronization data by performing reverse delay compensation based on the data processing delay; Determining the carbon fiber defect synchronization data according to the defect point spatial coordinates and the multimodal time synchronization data; The carbon fiber defect synchronization data is strategically integrated to obtain preliminary winding status determination data, specifically including: Performing basic confidence level configuration analysis on the carbon fiber defect synchronization data to obtain modal confidence levels corresponding to various modes; wherein the modal confidence levels include: eddy current modal confidence level, mechanical impedance modal confidence level, and acoustic emission modal confidence level; Based on the modal confidence, first evidence synthesis data is determined through reliability weight correction; Performing dynamic weight optimization on the first evidence synthesis data to obtain second evidence synthesis data; The preliminary determination data of the entanglement state is obtained by synthesizing the data based on the second evidence and performing modal conflict resolution analysis.

7. A non-volatile computer storage medium for real-time defect detection based on carbon fiber winding, storing computer-executable instructions, characterized in that: The computer executable instructions are configured to: Acquiring real-time carbon fiber winding environmental data, and performing multimodal collaborative detection on the real-time carbon fiber winding environmental data to obtain a real-time carbon fiber defect data set; performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data group to determine carbon fiber defect synchronization data; Performing strategic-level fusion on the carbon fiber defect synchronization data to obtain preliminary winding state determination data; Based on the preliminary winding state determination data, multimodal weight update data is determined by analyzing the process stages of carbon fiber winding; According to the modal weight update data, the real-time winding defects of the carbon fiber are determined by analyzing the carbon fiber defect types; The carbon fiber winding real-time environmental data also includes: carbon fiber layup, local mechanical vibration data, and broadband acoustic emission signals; Multimodal collaborative detection is performed on the carbon fiber winding real-time environmental data to obtain a carbon fiber real-time defect data set, specifically including: Performing high-frequency eddy current array inversion analysis on the carbon fiber ply to obtain carbon fiber ply state data; wherein the carbon fiber ply state data includes: fiber arrangement direction and deep defect data; Performing surface vibration response analysis on the local mechanical vibration data to obtain local mechanical impedance; wherein the surface vibration response analysis includes: frequency sweep mechanical vibration processing and surface laser vibrometer analysis; Based on the broadband acoustic emission signal, determining ply defect type parameters through acoustic print feature recognition; Obtaining the carbon fiber real-time defect data set according to the carbon fiber ply state data, the local mechanical impedance, and the ply defect type parameter; Performing multimodal spatiotemporal synchronization calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronization data specifically includes: Performing multimodal spatial calibration on the real-time defect data of the carbon fiber, and performing feature coordinate conversion on the feature coordinates obtained by the multimodal spatial calibration to obtain the spatial coordinates of the defect point; wherein the feature coordinate conversion includes: electrical marker point coordinate conversion, stiffness mutation zone coordinate conversion, and voiceprint positioning coordinate conversion; Acquire a data processing delay of a preset sensor, and obtain multimodal time synchronization data by performing reverse delay compensation based on the data processing delay; Determining the carbon fiber defect synchronization data according to the defect point spatial coordinates and the multimodal time synchronization data; The carbon fiber defect synchronization data is strategically integrated to obtain preliminary winding status determination data, specifically including: Performing basic confidence level configuration analysis on the carbon fiber defect synchronization data to obtain modal confidence levels corresponding to various modes; wherein the modal confidence levels include: eddy current modal confidence level, mechanical impedance modal confidence level, and acoustic emission modal confidence level; Based on the modal confidence, first evidence synthesis data is determined through reliability weight correction; Performing dynamic weight optimization on the first evidence synthesis data to obtain second evidence synthesis data; The data of the second evidence synthesis is analyzed through modal conflict resolution to obtain preliminary determination data of the entanglement state.

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